Utility transformation guided by improved customer insight.
Osvald Bjelland is chairman and CEO of business advisory firm Xyntéo. Bjelland launched the Global Leadership and Technology Exchange Partnership, a Xyntéo initiative that brings together leading businesses in a partnership that aims to reinvent growth.
Electricity consumers are changing. Their expectations for reliability and customer service levels are higher than ever and continue to grow. Solar PV and other end-use generation technologies are becoming an attractive and affordable option for an increasing number of customers. And the use of smart meters is becoming ever more widespread in the wake of rollout programs across the United States.
As a consequence, the ground is shifting under the feet of utilities – and regulators. Recent publications, including from the Edison Electric Institute,1 and public statements made by utility executives such as David Crane, CEO of NRG,2 show how concerned the incumbents are becoming and how unsustainable the status quo is perceived to be. The question is not if, but how utilities – especially vertically integrated utilities – can transform into market- and customer-oriented companies.
A key component in making this transition is to develop more and better customer insight that will enable utilities to be much more targeted and strategic in the way they engage customers.
New Opportunities, New Threats
We have analyzed the implications for utilities of four classes of end-user offerings – energy efficiency retrofits, distributed generation, electric vehicles, and distributed storage. Taking Florida as a case study (see Figure 1), we project that by 2030, more than two-thirds of residential customers will have installed some form of energy efficiency measure in their properties, and 7 percent will own a solar PV system. A fifth of residential customers will have adopted a combination of more than one end-use technology. Each of these individual purchasing decisions by customers – more than 7 million over a 20-year period – represents both an opportunity and a threat for existing utilities.
Improved energy efficiency and increased distributed generation will on the one hand mean that generation from central power plants would decline by more than 4 percent from what would be expected for the equivalent consumers using today’s technology mix, with negative implications for utility revenues. At the same time the value of the market for energy services to the residential sector in Florida will exceed $1 billion annually by 2030 – enough to more than compensate for the loss of revenue from centralized generation.
In addition to the direct revenue effects, this more proactive consumer base also presents an opportunity to build stronger brands and increase customer loyalty (and lower churn and customer acquisition costs) by offering customers a broader range of energy services and products. While the data we refer to here reflects the probable effects for residential customers, the same imperatives are affecting larger commercial and industrial customers, as well as small and medium-sized businesses. Indeed, the effects of these emerging technologies on customer behavior for these critical loads will drive the urgency for utilities to adapt to the evolving marketplace.
However, it isn’t only traditional utilities that are in a position to compete for this new revenue, and some of the most pressing threats to incumbent utilities are emerging from outside the sector. Successful new entrants like SolarCity have already shown that they can be very effective in combining new technologies with innovative business models that respond to customer needs; 75 percent of new solar systems installed are now under 20-year leasing contracts that give companies such as SolarCity a long-term customer partnership. Similarly, companies like Verizon and Comcast have moved into the energy space from the mobile phone and cable services respectively. Verizon’s new smart home product allows consumers to manage thermostats and lighting remotely. By bundling energy management with security, Verizon is adding value for its customers who wouldn’t necessarily focus on their energy consumption alone. Comcast’s EcoSaver product is taking a similar approach.
Utilities aren’t sitting still, of course. In a mapping of 50 leading utilities and electricity retailers in the United States, Europe, and Australia, we found an emerging picture of a rich diversity in offerings and solutions by incumbent utilities, as well as regional differences. Overall, the picture is one where U.S. electricity companies offer on average more products than their European and Australian peers. For example, around two-thirds of the mapped U.S. utilities and retailers provide peer-comparisons on energy use, while only a quarter of European utilities do. Similarly, more than 80 percent of the U.S. utilities in the sample offer rebates for energy efficiency, while this is true for only a third of European utilities. Utilities and retailers like SDG&E, Duke, and ComEd are leading in terms of number of energy efficiency solutions, while PG&E, SDG&E, and Duke have the richest demand response programs. On the other hand, European utilities are ahead with regards to distributed generation solutions (solar) and electric vehicles. In Europe, more than 60 percent of the utilities analyzed offer solar PV, typically by selling and installing PV systems. Most solar PV installations are occurring in a small number of U.S. states with strong policy frameworks in place. Development is less concentrated in Europe and feed-in tariffs in some countries (Germany and Italy in particular) have been remarkably generous against a background of falling system costs.
Despite this breadth of activity, however, our conversations with utilities around the world suggest that current practice for many is to launch new services and products tentatively and at a small scale. Often the motivation is limited to regulatory compliance. In other cases they might be driven by a desire to prevent small insurgent firms from entering the market for energy services within the incumbent’s geography. In today’s rapidly changing context this will no longer do. Rather than introduce new products at the fringes of their customer offering, utilities must confront their fear of cannibalizing their traditional business and fundamentally transform into energy solutions companies that put their customers’ needs in the center. Without such a strategy, incumbent utilities risk seeing their core business wither, while new and more agile entrants capture the growth in service and solutions opportunities.
The Power of Customer Insight
It’s one thing to speak of building a utility’s offerings around the needs of its customers; achieving it in practice is of course harder. A particular problem for many utilities is to understand what consumers’ preferences and behavior patterns actually are.
Utilities can maximize consumer engagement strategies by taking a structured and quantitative approach towards understanding retail consumer preferences – as is done in many other industries. Basing analysis on a detailed understanding of customers and their preferences has the advantage of allowing robust analysis of future trends, and is becoming more feasible and accurate with the emergence of more and better data.
While not all consumers are individually rational in their purchasing decisions, in aggregate one powerful predicator of customer behavior is their sensitivity to the payback period associated with a product – the number of years it takes to make financial savings equal to the upfront purchase cost of an investment. As an example, consider the data that is publicly available on the thousands of residential solar PV installations in California during the period 2007 through 2012.3 By combining the system cost information in this dataset with information on electricity tariffs, tax credits, and other subsidies for solar, it’s possible to estimate the payback period (assuming cash sales) for residential solar PV for each month in this period. By relating this to monthly sales figures, it’s possible to infer a relationship between system cost and installations, shown in Figure 2. As expected, this adoption curve is downward sloping – a longer payback period means lower sales. While this provides a powerful initial insight into consumer behavior, such an approach must also intelligently deal with the fact that consumer behavior and market conditions have dynamic components. The steepness and position of the curve of course could change as preferences, trends, and knowledge all evolve. Notably, the leasing model for financing solar PV also grew considerably during the period covered by the data in this example.
A key advantage of looking at consumer behavior bottom-up is that it lends itself very well to scenario-based strategy development. For example, one future uncertainty for solar PV in the future is the 30-percent federal tax credit for which new projects are eligible. This is due to expire in 2016. If this weren’t to be renewed, and system costs and electricity rates remained stable, then the payback period for residential solar in California would increase to 7.5 or 8 years, which based on consumer behavior observed in recent years would imply a drop in sales to around half the level achieved while the tax credit is in place. A strategy that’s informed by consumer preferences in this way has the potential to be far more robust than one based only on trend extrapolation.
While payback is one predicator of behavior, of course it isn’t the only one. Other important factors for many customers are their awareness of possible solutions and their understanding of it, their interest in managing their energy, their financial situation, and of course how neighbors or similar businesses behave. There is evidence that localized imitation is a factor, which means that you expect accelerated uptake in a given geography (neighborhood) once critical mass is reached. More sophisticated modeling also should take these effects into account.
While a single market-wide adoption curve of the sort shown in Figure 2 gives a useful insight into customer behavior, it doesn’t capture the different groups of customers that exist within the market. Understanding market segments is important both for estimating market size and for minimizing customer acquisition costs. This is particularly important in the retail context, where the cost of acquiring new customers makes the difference between a commercially viable and unviable business.
Numerous factors govern consumers’ propensity to adopt new technologies. Income is a major determinant of adoption, particularly for technologies with a large capital outlay, and access to credit also plays a role. Households experiencing fuel poverty – difficulty in heating their homes adequately in winter or cooling them in summer – can have markedly different adoption preferences, particularly for energy efficiency retrofits. Customers’ likelihood to adopt also is affected by the particular economics of their individual situation. For example, the type of building they inhabit becomes highly important. Older U.S. buildings offer far larger potential for energy efficiency retrofits than newer ones do (see Figure 3).
Local geography also plays an important role in adoption. Some commute patterns might lend themselves more toward electric vehicle adoption. The decision to install solar also can vary dramatically between different microclimates, roof aspects, and shading from trees and hillsides. This is especially important for example in coastal areas of California, where marine layer fog can dramatically affect the performance of a PV system.
A customer’s experience and interest in energy also is important. Behavioral segmentation can be used to target customers with experience in a particular product. For example, a customer owning an electric vehicle might be more likely to purchase a solar system. Likewise, a customer with little experience of energy products might be less likely to adopt an energy efficiency or dynamic pricing program.
Taking into account different characteristics of customers – their income, location, the age profile of their buildings and other relevant factors, allows for detailed scenario planning and a detailed understanding of customer behavior. For example, our analysis of Florida at a county level suggests that solar PV systems likely will be adopted to very different degrees in different counties due to economic, geographic, demographic, and building type differences across the state (see Figure 4).
Using Real-Time Data
Increasingly, the ability to use real-time data to assess consumer behavior and customize offerings will become a differentiator in utility customer service. Much as a communications company can alert customers of potential overages on their cell phone minutes, utilities will be able to increase engagement by using real-time information to notify and interact with their customers.
Examples of using real-time data include use of smart meter data, communications about weather and demand response events, and increasingly real-time and forecast information about solar production. This information can be used to communicate when to charge electric vehicles, when to turn on household appliances, or how to reduce energy usage during a demand response event. Increasingly the information can be used to advise customers on different rate plan options and to up-sell additional services. The IT challenges are real and shouldn’t be underestimated. However, utilities that successfully integrate real-time data will open up an additional communication channel to customers – one that new entrants are already experienced with.
Interactions Among Offerings
Often a customer’s adoption of one technology will change the economic case for adopting another. Consider for example a residential customer’s decision to purchase a solar PV system. If a homeowner has retrofitted insulation to the property – better wall and roof insulation, for example – then the average annual electricity consumption of the home might be considerably lower than an equivalent home with a standard level of insulation. Under a tiered-rate pricing tariff, the owner of the better-insulated home likely chose to install less solar than the owner of an equivalent standard-efficiency home because under a net-metering solar support regime, they are offsetting less of the more expensive grid power (see Figure 5). That isn’t to say that both energy efficiency and solar shouldn’t or won’t be installed together, but rather that their interactions affect investment timing, sizing, and location. Another example of technology interaction that could become relevant in future is that between distributed storage technologies and solar PV sizing; a household with battery storage might install a far bigger solar PV system than one without storage, particularly if PV tariffs strongly favor self-consumption over grid export.
Analysis that takes into account the economic decisions facing individual customer groups, and that considers the real-world interactions between these decisions, inevitably involves a degree of complexity – in modeling the stocks and flows of buildings and technology choices for example. But the result is a richer and more accurate representation of customers’ decision-making and a more solid foundation for strategy development.
By taking a full range of quantifiable factors into account, it’s possible to project the different levels of technology adoption and engagement within different consumer groups. Individual consumers might not be consistent or rational in their individual purchasing decisions, but in aggregate it’s possible to analyze both their preferences and the economic implications of their options, and draw robust conclusions about their future behavior.
In this rapidly shifting environment, the successful utilities will be those most skilled at developing offerings that their customers want, at the right time, and delivering them with the right business model. Developing capabilities to better understand and predict customer behavior and preferences will be a success factor in navigating the transformation underway. While there’s no crystal ball available, more sophisticated and continuously improved customer predictive analytics is one way incumbent utilities can improve their strategies and offerings. By being more targeted and forceful in implementing these new initiatives, utilities can successfully make the transition into truly consumer-orientated companies, and thus be better positioned to both protect their core business and at the same time compete effectively for new services.
3. Go Solar California website: http://www.californiasolarstatistics.ca.gov/